| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
| uniform sampler2D swapTexture; | |
| uniform bool shaderIsActive; | |
| void main() | |
| { | |
| vec4 pixel = texture2D(bitmap, openfl_TextureCoordv); | |
| if (!shaderIsActive) | |
| { | |
| gl_FragColor = pixel; |
| package effects; | |
| import openfl.display.BitmapData; | |
| import flixel.util.FlxColor; | |
| import flixel.system.FlxAssets.FlxShader; | |
| /** | |
| * https://gamedevelopment.tutsplus.com/tutorials/how-to-use-a-shader-to-dynamically-swap-a-sprites-colors--cms-25129 | |
| * https://github.com/HaxeFlixel/flixel-demos/blob/master/Effects/BlendModeShaders/source/openfl8/effects/ColorSwapEffect.hx | |
| * https://community.openfl.org/t/passing-an-array-as-parameter-to-shader/9932/9 |
Caution
As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.
cPanel does not provide an official uninstaller. If a cPanel installation fails, the usual recommendation is to reinstall the entire server.
However, it is possible to clean the existing installation and reinstall cPanel without formatting the server.
This guide explains how to remove the broken installation and perform a fresh install.
This process removes cPanel, MySQL/MariaDB, and all related data. Only use it on servers where data loss is acceptable or backups exist.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Tailscale version 1.54 or later used with OpenWrt 24.10 or later (which uses kernel 6.6) enables UDP throughput improvements via transport layer offloading.
Namely, tuning two features may show improved throughput:
rx-udp-gro-forwarding: Enables UDP Generic Receive Offload (GRO) forwarding, which aggregates incoming UDP packets to reduce CPU overhead on receive.rx-gro-list: If disabled (off), it prevents multiple flows from being aggregated simultaneously which simplifies flow handling and performance on some workloads.[!NOTE]